The ModelNet40 dataset contains synthetic object point clouds. As the most widely used benchmark for point cloud analysis, ModelNet40 is popular because of its various categories, clean shapes, well-constructed dataset, etc. The original ModelNet40 consists of 12,311 CAD-generated meshes in 40 categories (such as airplane, car, plant, lamp), of which 9,843 are used for training while the rest 2,468 are reserved for testing. The corresponding point cloud data points are uniformly sampled from the mesh surfaces, and then further preprocessed by moving to the origin and scaling into a unit sphere. Source: Geometric Feedback Network for Point Cloud Classification
ModelNet40-C is a comprehensive dataset to benchmark the corruption robustness of 3D point cloud recognition. We create ModelNet40-C based on the ModelNet40 validation set with 15 corruption types and 5 severity levels for each corruption type including density, noise, and transformation corruption patterns. Our dataset contains 185,000 distinct point clouds that help provide a comprehensive picture of model robustness.
PointCloud-C is the very first test-suite for point cloud robustness analysis under corruptions. - Two sets: ModelNet-C for point cloud classification and ShapeNet-C for part segmentation. - Real-world corruption sources, ranging from object-, senor-, and processing-levels. - Seven types of corruptions, each with five severity levels. - Benchmark with more than 20 point cloud recognition algorithms. - Methods ranging from architecture design, augmentations, and pre-training.
JetClass is a new large-scale dataset to facilitate deep learning research in particle physics. It consists of 100M particle jets for training, 5M for validation and 20M for testing. The dataset contains 10 classes of jets, simulated with MadGraph + Pythia + Delphes. A detailed description of the JetClass dataset is presented in the paper Particle Transformer for Jet Tagging. An interface to use the dataset is provided here.